Federal Trade Commission (FTC) indicated that the phone scam is the
most popular type of scams in United States and there are more than 150
millions of disputes. In particular, in 2014, the amount of monetary loss from
the phone scam is more than 1.7 billion. 46% of victims can clearly indicate
how they are tricked but 54% of victims claimed to be tricked by phone scam. At
the same time, the strike against the robocalls by FTC is supported by primary companies
such as AT&T and Google, etc. On the other hand, there are cases where the
college students in China are tricked to give away their tuition fees.
According to the report, approximately 1.6 millions of people are conducting
the business of the scam and the revenue of this business is more than 110
billion RMB. In addition, there are similar cases in Japan where the amount of
monetary loss is more than 50 billion Yen. As shown in Fig. 1, in the case of
non-repeated calling numbers, all of the phone scams intensively occur during
the weekdays. More specifically, compared to such an intensive amount of phone
scams during the weekdays, only approximately one third of phone scams occurs
during the weekend in US, India, and Taiwan. In the case of repeated calling
numbers, the situation remains unchanged; very little portion of phone scams
occurs during the weekend.

FTC announced a list of dialing numbers for reference; once the call
is from those numbers, it is likely to be a harassing call. Nonetheless, the
reality is far more complicated; for example, the list announced by FTC is not
working for the call from the non-US area. Actually, phone scams can be
categorized as follows:1) Free Vacations and Prizes2) Loan Scams3) Phony Debt Collectors4) Fake Charities5) Medical Alert/Scams6) Targeting Seniors7) Warrant Threats8) IRS Calls

For phone scams, our core product has
80 million daily active users, and 23 million daily active users have received
phone call from strangers which including 10% phone scams. Fig. 13 and Fig. 14 show the
average of collection of phone numbers in 1 day. We found that during the
working time, the so-called malicious calling such as harassing, telemarketing,
scams and insurance could be a huge portion. We also found that the normal
calling from, for example, express, service center, occupy only less than 10%. According to the data above, we create and adapt our
DNN model.

Fig. 13 / Fig. 14

Fig. 15 shows our mini experiment
results, which are analyzed by Logistic Regression, Decision Tree, Random Forest
and SVM algorithms with DNN. We found that accuracy and precision of DNN are
more stable. Thus we include more countries in out experiment, according to our
experiment, we can perceive except for the arithmetic mean of deep neural
network has reached 85%, the rests of the Logistic Regression, Decision Tree,
Random Forest algorithm only reach between 70% to 80%, and SVM only reached 75%
(show as Fig. 16).

Fig. 15 | Fig. 16

Moreover, the standard deviation is
applied to evaluate the stability of each learning method. Our DNN model has a
low standard deviation which indicates the data points tend to be close to the
mean. With the consideration of long term defense and system maintenance of
phone scams and the consideration of the detection accuracy of deep neural
network, we are pretty sure that the adapted deep learning approach is better
than conventional machine learning approaches.

The future work is the improvement of
our deep learning model and reduce the complex task and train a high performance
to faced the phone scams from a huge amount of computation burden.